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Evaluating Mutation-based Fault Localization for Quantum Programs

Yuta Ishimoto, Masanari Kondo, Naoyasu Ubayashi, Yasutaka Kamei, Ryota Katsube, Naoto Sato, Hideto Ogawa

TL;DR

This work evaluates mutation-based fault localization (MBFL) for quantum programs written in Qiskit, comparing real-world faults (Bugs4Q) with artificial faults (BugsAqua) and contrasting quantum versus classical mutation operators. It finds that real-world faults are harder for MBFL, with higher EXAM scores and greater variability, while quantum mutation operations more often produce behavior-changing mutants, particularly in real-world faults. The study also contrasts MBFL with spectrum-based fault localization (SBFL), showing MBFL can outperform SBFL in worst-case scenarios and offering broader applicability when faults arise in programs mixing classical and quantum instructions. Overall, MBFL shows potential for quantum software debugging but requires richer mutation operators and targeted strategies to handle real-world fault patterns.

Abstract

Quantum computers leverage the principles of quantum mechanics to execute operations. They require quantum programs that define operations on quantum bits (qubits), the fundamental units of computation. Unlike traditional software development, the process of creating and debugging quantum programs requires specialized knowledge of quantum computation, making the development process more challenging. In this paper, we apply and evaluate mutation-based fault localization (MBFL) for quantum programs with the aim of enhancing debugging efficiency. We use quantum mutation operations, which are specifically designed for quantum programs, to identify faults. Our evaluation involves 23 real-world faults and 305 artificially induced faults in quantum programs developed with Qiskit(R). The results show that real-world faults are more challenging for MBFL than artificial faults. In fact, the median EXAM score, which represents the percentage of the code examined before locating the faulty statement (lower is better), is 1.2% for artificial benchmark and 19.4% for the real-world benchmark in the worst-case scenario. Our study highlights the potential and limitations of MBFL for quantum programs, considering different fault types and mutation operation types. Finally, we discuss future directions for improving MBFL in the context of quantum programming.

Evaluating Mutation-based Fault Localization for Quantum Programs

TL;DR

This work evaluates mutation-based fault localization (MBFL) for quantum programs written in Qiskit, comparing real-world faults (Bugs4Q) with artificial faults (BugsAqua) and contrasting quantum versus classical mutation operators. It finds that real-world faults are harder for MBFL, with higher EXAM scores and greater variability, while quantum mutation operations more often produce behavior-changing mutants, particularly in real-world faults. The study also contrasts MBFL with spectrum-based fault localization (SBFL), showing MBFL can outperform SBFL in worst-case scenarios and offering broader applicability when faults arise in programs mixing classical and quantum instructions. Overall, MBFL shows potential for quantum software debugging but requires richer mutation operators and targeted strategies to handle real-world fault patterns.

Abstract

Quantum computers leverage the principles of quantum mechanics to execute operations. They require quantum programs that define operations on quantum bits (qubits), the fundamental units of computation. Unlike traditional software development, the process of creating and debugging quantum programs requires specialized knowledge of quantum computation, making the development process more challenging. In this paper, we apply and evaluate mutation-based fault localization (MBFL) for quantum programs with the aim of enhancing debugging efficiency. We use quantum mutation operations, which are specifically designed for quantum programs, to identify faults. Our evaluation involves 23 real-world faults and 305 artificially induced faults in quantum programs developed with Qiskit(R). The results show that real-world faults are more challenging for MBFL than artificial faults. In fact, the median EXAM score, which represents the percentage of the code examined before locating the faulty statement (lower is better), is 1.2% for artificial benchmark and 19.4% for the real-world benchmark in the worst-case scenario. Our study highlights the potential and limitations of MBFL for quantum programs, considering different fault types and mutation operation types. Finally, we discuss future directions for improving MBFL in the context of quantum programming.
Paper Structure (19 sections, 2 equations, 2 figures, 2 tables)

This paper contains 19 sections, 2 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Study Design
  4. Research Questions
  5. Bug Benchmarks
  6. Real-world Benchmark (Bugs4Q)
  7. Artificial Benchmark (BugsAqua)
  8. Mutating Buggy Programs
  9. Suspiciousness Scores
  10. Evaluation Metrics for Fault Localization
  11. Experimental Environment
  12. Results
  13. RQ1: Artificial vs. Real-world Faults
  14. RQ2: Quantum vs. Classical Mutation Operations
  15. Discussion
  16. ...and 4 more sections

Figures (2)

  • Figure 1: Our study design.
  • Figure 2: Empirical cumulative distribution function of EXAM scores for real-world faults (Bugs4Q) and artificial faults (BugsAqua). The filled areas indicate the range between the best-case and worst-case EXAM scores.